

from math import pi, sqrt


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if num <= 0:
						raise ValueError("""math domain error""")
			if num > 171.5:
						raise OverflowError("""math range error""")
			elif num - int(a__) not in (0, 0.5):
						raise NotImplementedError("""num must be an integer or a half-integer""")
			elif num == 0.5:
						return sqrt(a__)
			else:
						return 1.0 if num == 1 else (num - 1) * gamma(num - 1)


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			assert gamma(0.5) == sqrt(a__)
			assert gamma(1) == 1.0
			assert gamma(2) == 1.0


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	__snake_case				:      Union[str, Any]   		=			1.0
	while num:
		__snake_case				:      Optional[Any]   		=			float(input("""Gamma of: """))
		print(F"""gamma({num}) = {gamma(num)}""")
		print("""\nEnter 0 to exit...""")





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:      int   		=			{
    """configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      int   		=			["""LlamaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Union[str, Any]   		=			[
	    """LlamaForCausalLM""",
	    """LlamaModel""",
	    """LlamaPreTrainedModel""",
	    """LlamaForSequenceClassification""",
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	__snake_case				:      Dict   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


